What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why are OpenRouter users still hardcoding a pricing list that goes stale the moment a provider changes rates?

OpenRouter's own API response carries real-time cost data per call, but downstream tools like LiteLLM maintain static price lists instead of reading it, making cross-provider cost tracking unreliable.

Category: AI / Agents · Trend: LLM · Opportunity score: 6.0 / 10

What is the “Why are OpenRouter users still hardcoding a pricing list that goes stale the moment a provider changes rates?” problem in 2026?

Who has this problem?

Developers building multi-provider LLM gateways for cost tracking.

Recorded source context

Dataset source note: OpenRouter pricing is dynamic and varies based on the underlying provider, making hardcoded costs unreliable for accurate expense tracking.

This note may summarize the referenced material rather than quote it verbatim. Source label: BerriAI/litellm GitHub issue #13653, 15 Aug 2025. (reference).

Existing players in this space

LiteLLM static price maps: Require manual updates whenever a provider changes rates.
OpenRouter dashboard: Accurate for OpenRouter usage but not merged with other provider spend.

What existing players are missing

A gateway that reads live per-call cost fields straight from provider API responses instead of maintaining a static price table, so multi-provider spend dashboards never drift out of sync.

How Real Problem AI scores this opportunity

Aggregate score: 6.0 / 10. Four-axis rubric:

Problem severity: 5 / 10
AI feasibility today: 8 / 10
Market signal: 5 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Provider response cost-field parsing
Multi-provider cost normalization
Live pricing cache with fallback
Unified spend dashboard